A facial recognition system is utilized for automatic identification or verification of a person based on a digital image or a video frame. Facial recognition systems are seeing increasing use for purposes including security (e.g., as used in security systems to identify criminals caught committing a crime on camera or as used to identify faces matching mugshots). As an example, at Super Bowl XXXV in January 2001, police in Tampa Bay, Florida, used facial recognition software to search for potential criminals and terrorists in attendance at the event. Other uses for facial recognition software may include, e.g., user identification (i.e., as a login method instead of or in addition to biometric identification or other forms of identification), categorizing images and videos (e.g., tagging or grouping images based on identification of a particular user's face), and the like.
One of the most common solutions for facial recognition systems is comparing selected facial features from an image or video frame to features in a facial database. Some facial recognition systems identify faces or portions of faces by extracting landmarks, or features, from an image of a user's face. For example, the relative position, size, and/or shape of the user's eyes, nose cheekbones, jaw, or other parts of the user's face may be analyzed. These features are used to search for other images with matching features. Other solutions normalize a gallery of face images and then compress the face data, only saving the data in the image that is useful for facial recognition.
One disadvantage of existing facial recognition solutions is that, although such solutions typically do not require the cooperation of the test subject (e.g., the test subject specifically posing for an image to be analyzed), thereby enabling potential for mass facial recognition, such solutions may still face challenges in effectively recognizing faces for large groups of people (e.g., as needed for railway and airport security). In particular, challenges faced by facial recognition systems include difficulties in identifying faces at an angle (e.g., more than 20 degrees from a frontal view), misidentification, and difficulties identifying faces when visibility is low (e.g., poor lighting, objects or facial hair blocking the face, etc.). Thus, improved techniques for facial recognition would be desirable.
It would therefore be advantageous to provide a solution that would overcome the challenges noted above.